research
∙
09/29/2022
Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
The reconstruction loss and the Kullback-Leibler divergence (KLD) loss i...
share
research
∙
04/01/2022
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
Graphs are a powerful tool for representing and analyzing unstructured, ...
research
∙
05/14/2020
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
Large-scale population-based studies in medicine are a key resource towa...
research
∙
05/09/2020
Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions
Today, cataract surgery is the most frequently performed ophthalmic surg...
research
∙
05/02/2020
Decision Support for Intoxication Prediction Using Graph Convolutional Networks
Every day, poison control centers (PCC) are called for immediate classif...
research
∙
03/31/2020
Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time
Computer-aided diagnosis (CADx) algorithms in medicine provide patient-s...
research
∙
03/27/2020
Latent Patient Network Learning for Automatic Diagnosis
Recently, Graph Convolutional Networks (GCNs) has proven to be a powerfu...
research
∙
05/08/2019
Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets
Clinical diagnostic decision making and population-based studies often r...
research
∙
05/08/2019
Adaptive image-feature learning for disease classification using inductive graph networks
Recently, Geometric Deep Learning (GDL) has been introduced as a novel a...
research
∙
03/11/2019
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Geometric deep learning provides a principled and versatile manner for t...
research
∙
02/05/2019
Stabilizing Inputs to Approximated Nonlinear Functions for Inference with Homomorphic Encryption in Deep Neural Networks
Leveled Homomorphic Encryption (LHE) offers a potential solution that co...
research
∙
03/30/2018
Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion
In large population-based studies and in clinical routine, tasks like di...
research
∙
01/26/2018
Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning
In recent years, Generative Adversarial Networks (GAN) have emerged as a...
research
∙
02/20/2017
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
Automatic segmentation of the liver and hepatic lesions is an important ...
research
∙
10/07/2016
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Automatic segmentation of the liver and its lesion is an important step ...
research
∙
06/15/2016
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Convolutional Neural Networks (CNNs) have been recently employed to solv...
research
∙
01/26/2016
